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A non-intrusive Polynomial Chaos Expansion (PCE) method is used to predict uncertainty regression models of the various parameters that make up a curvefit of the FRF: natural frequencies, damping ratios, complex amplitudes, mass and stiffness residuals, by making use of an efficient Latin Hypercube technique.
This effect can be more easily explained: Neutral cues predict uncertainty, resulting in more cautious response strategies for both congruent and incongruent trials.
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Data variability is of interest to predict uncertainties and refinement options.
A method is introduced to predict uncertainties of the B-spline freeform surfaces that are reconstructed from the measurement points for inspection of freeform surfaces.
For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the historical input data.
Prediction task also predicts uncertainty of the robot pose.
But he also predicted uncertainty about the future of that country.
Symbol size is proportional to the magnitude of the observed error or predicted uncertainty.
Note that the two predicted uncertainty profiles bracket 0.5 at the refined threshold.
The uncertainty analysis showed that predicted uncertainty is the main source for combined uncertainty.
Here again, the less reliable model signals its weakness by having a predicted uncertainty profile that crosses its voting threshold well below 0.5.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com